Closed loop deep brain stimulation systems and methods

ABSTRACT

The present disclosure relates generally to systems, methods, and devices for closed loop deep brain stimulation. In particular, a neural signal is measured and provided to software. The software includes a feature generator and a brain network model that takes the neural signal and estimates other neural signals that are not directly measured, and operates as a model of the brain. The software determines a stimulation signal to be sent to stimulating electrodes. Estimated signals by the brain network model are continuously compared to actual signals from the brain. The closed loop feedback system advantageously allows for electrical stimulation levels and patterns to be continuously updated while delivered to a patient.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/169,835, filed Jun. 2, 2015, the entirety of which is herebyincorporated by reference.

BACKGROUND

The present disclosure relates generally to systems, methods and devicesfor closed loop deep brain stimulation. Briefly, a model of the brain isformed in software and is continuously updated by neural signals. Themodel is used to determine stimulation signals sent to the human brain,and the resulting neural signals are used to provide feedback to thesoftware model of the brain.

Millions of people suffer from neurological disorders such asParkinson's Disease (PD), epilepsy, essential tremor, dystonia,depression, and obsessive-compulsive disorder. One treatment for theseconditions is deep brain stimulation (DBS), wherein a brain pacemaker isimplanted into the brain of a patient. The brain pacemaker sendselectrical impulses to specific parts of the brain to treat theneurological disorder. DBS can be very effective at managing thesymptoms of Parkinson's, which consequently greatly improves thepatient's quality of life. DBS may be helpful for certain cases ofepilepsy by reducing the frequency and severity of epileptic seizures.

Some commercial brain pacemaker devices have the capability to bothelectrically stimulate the brain and also sense electrical signals fromthe brain. However, the devices currently lack working closed loopfeedback. Because the devices lack working closed loop feedback, theelectrical stimulation delivered by the device must be manually adjustedin repeated office visits. An example of such a device is the MedtronicBrain Radio. It would be desirable to provide fully automaticclosed-loop systems that provide feedback (use the output signal as aninput) so that effective deep brain stimulation is provided.

BRIEF SUMMARY

The present disclosure relates to systems and methods that can be usedto automatically adjust for spatial and/or temporal patterns in apatient's brain during deep brain stimulation. These methods improve theefficacy of the stimulation, maintain that efficacy over time, and mayreduce overall power consumption. A closed-loop feedback mechanism ispresent in a software model of the brain, and permits the system toautomatically adjust the electrical stimulation being provided to thepatient.

Disclosed herein are methods for adjusting deep brain stimulation,comprising: (a) measuring neural signals in a target area of the brainof a user using sensing electrodes; (b) providing the measured neuralsignals to a Feature Generator and to a Brain Network Model; (c) usingthe Brain Network Model to estimate neural signals not directlymeasurable by the sensing electrodes, and sending the estimated neuralsignals to the Feature Generator; (d) generating features from themeasured neural signals and the estimated neural signals with theFeature Generator, and sending the features to at least one decoder; (e)calculating a feedback index level using output from the at least onedecoder; (f) determining an error level between the feedback index leveland a desired index level; (g) based on the error level, using a MIMOcontroller to send a stimulation signal to stimulating electrodes in thetarget area of the brain; and (h) repeating steps (a)-(g) until theerror level between the feedback index level and the desired index levelis below a desired value.

The measured neural signals can include single unit action potentials,multi-unit activity, local field potential, signal power across at leastone frequency band, and coherence between signals. The sensingelectrodes can be implanted or can be placed on the scalp of the user.The sensing electrodes can be placed in the basal ganglia, the motorcortex, the prefrontal cortex, the subthalamic nucleus, the externalglobal pallidus, or the internal global pallidus of the user.

The generated features may include signal amplitude, amplitude of thesignal in a given frequency range, amplitude of the signal in a waveletscale, a firing rate, single unit action potentials, multi-unitactivity, local field potential, signal power across at least onefrequency band, and coherence between signals.

The methods may further comprise measuring a neurotransmitter level, andsending the measured neurotransmitter level to the at least one decoder.In particular embodiments, the measured neurotransmitter is dopamine,serotonin, or norepinephrine.

The at least one decoder can calculate a condition severity index, animpulsive behavior index, a depressive state index, or an anxiety stateindex.

The stimulation signal can also be sent to the Brain Network Model. TheBrain Network Model may be validated using system identification andoptimization techniques. Such system identification and optimizationtechniques may be selected from the group consisting of regressiontechniques, genetic algorithms, pattern searches, and Nelder-Mead. Inparticular embodiments, the at least one decoder has been trained usinga condition severity index, a depressive state index, or an anxietystate index determined by a physician.

The methods may further comprise reducing a difference between themeasured neural signals and corresponding estimated neural signals byadjusting the Brain Network Model. Thus, future simulations/calculationsof the brain state can be more accurate.

Also disclosed are systems for adjusting deep brain stimulation,comprising: (a) sensing electrodes configured to measure neural signalsin a target area of a brain of a patient; (b) a Feature Generator whichreceives the measured neural signals from the sensing electrodes; (c) aBrain Network Model which receives the measured neural signals from theelectrodes and estimates neural signals not directly measurable by thesensing electrodes; (d) multiple decoders which receive generatedfeatures from the Feature Generator and which calculate a feedback indexlevel; and (e) a MIMO controller which determines an error between thefeedback index level and a desired index level and which send astimulation signal to stimulating electrodes based on the error; whereinthe MIMO controller is configured to continually determine a new errorbetween a new feedback index level and the desired index level and senda new stimulation signal to stimulating electrodes based on the newerror until the new error is below a desired value.

The DBS systems, methods and devices described herein include closedloop feedback. Desirably, this reduces the number of required visits toa physician for patients using these DBS systems. Other advantages maybecome apparent to one of ordinary skill in the art upon reading andunderstanding this disclosure. It is to be understood that a specificembodiment may attain none, one, two, more, or all of these advantages.

BRIEF DESCRIPTION OF THE DRAWINGS

The following is a brief description of the drawings, which arepresented for the purposes of illustrating the exemplary embodimentsdisclosed herein and not for the purposes of limiting the same.

FIG. 1 is a diagram illustrating a Deep Brain Stimulation (DBS) system.

FIG. 2 illustrates the closed loop feedback that is implemented within amultiple input multiple output (MIMO) controller of the presentdisclosure.

FIG. 3 illustrates an aspect of a brain network model (BNM).

DETAILED DESCRIPTION

A more complete understanding of the methods and apparatuses disclosedherein can be obtained by reference to the accompanying drawings. Thesefigures are merely schematic representations and are not intended toindicate relative size and dimensions of the assemblies or componentsthereof.

Although specific terms are used in the following description for thesake of clarity, these terms are intended to refer only to theparticular structure of the embodiments selected for illustration in thedrawings, and are not intended to limit the scope of the disclosure. Inthe drawings and the following description below, it is to be understoodthat like numeric designations refer to components of like function.

The present disclosure relates to systems, methods and devices forproviding Deep Brain Stimulation (DBS) that include closed loopfeedback. These would be useful for patients who may have neurologicaldisorders such as Parkinson's Disease (PD), epilepsy, essential tremor,Tourette syndrome, and so forth that can be treated using deep brainstimulation. They may also be useful in treating other conditions suchas depression, obesity, Alzheimer's disease, chronic pain, and others.

Referring first to FIG. 1, a deep brain stimulation system includeselectrodes 152 that are implanted into the brain of a humanpatient/user/subject 110. The electrodes 152 are used to sense/measureneural signals from the brain, and to deliver electrical pulses as well.This information is sent to a brain pacemaker (not pictured) which sendscontrol/stimulation signals to the electrodes. The brain pacemaker doesnot need to be implanted inside the head, and is usually implantedsubcutaneously below the clavicle or the abdomen. The electricalstimulation pattern sent to the electrodes is determined by a programwhich can be implemented in hardware or software within the brainpacemaker.

FIG. 1 also illustrates a flowchart/algorithm for measuring a user'sneural activity, determining the state of the various neurons in thebrain and their electrical patterns, and translating that into anelectrical stimulation pattern that can be transmitted to move theelectrical patterns of the brain towards a desired state. First, theneural activity of the user/patient is measured to obtain measuredneural signals 112 from sensing electrodes. The measured neural signalsare sent to both a Feature Generator 120 and to a Brain Network Observer150. The Brain Network Observer 150 contains a Brain Network Model whichis used to estimate neural signals that were not directly measured. Verygenerally, the Brain Network Model estimates the neural signals thatwould have been generated by other neurons. These estimated neuralsignals 113 are also sent to the Feature Generator 120. The FeatureGenerator 120 then extracts features from the measured neural signalsand the estimated neural signals. Subsequently, the extracted feature(s)are sent to decoders 118. In some embodiments, neurotransmitter levelsare also measured by sensors in operation 116, and those levels are alsosent to the decoders 118. The decoders are used to determine variousindices and determine one or more feedback index levels, which is sentto a Multiple Input Multiple Output (MIMO) controller 122. The feedbackindex level(s) is/are compared to a desired index level(s) 124 todetermine one or more error level(s). The MIMO controller thendetermines the stimulation signal(s) 114 that should be sent to theelectrodes to reduce the error levels. The stimulation signal(s) is/aresent to electrodes to stimulate the brain. This algorithm repeats at ahigh rate, permitting continuous real-time updating of the stimulationsignal to the target. Thus, it should be understood that the methodsdescribed herein may be performed either continuously or intermittently.

The measured neural signals 112 are measured using sensing electrodes.The same electrodes may be used as both the sensing electrodes and thestimulating electrodes (e.g. such electrodes may alternate betweenstimulating and sensing). Alternatively, different electrodes may beused for stimulating than are used for sensing. Any suitable electrodearray may be used. For example, a “Utah array” of electrodes, such asthat made by Blackrock Microsystems, may be used. The Utah array canhave up to 96 electrodes. Also contemplated is the use of a “Michiganarray” of electrodes, such as that made by NeuroNexus. Customelectrode(s) are also contemplated that may have multiple modalities ofrecording, stimulation and chemical sensing (through a functionalcoating) for neurotransmitters. These electrode arrays record “brainwaves,” more particularly neural signals which are representative of avaried set of mental activities. These neural signals are sentwirelessly or, alternatively, through a wired connection, to a neuralsignal processing device for processing of the neural signals.

The sensing electrodes may sense electrical signals from different partsof the brain including the basal ganglia (BG) or the motor cortex.Sensing the electrical activity in these parts of the brain isespecially useful in treating Parkinson's disease and essential tremor.The BG nuclei include the subthalamic nucleus (STN), the external globuspallidus (GPe), and the internal globus pallidus (GPi), which all playan important role in the pathophysiology of PD and associated networkdynamics. The BG acts as a “relay station” for neural activity in thebrain, so it may be advantageous to both sense and stimulate in the BG.In addition to the BG, the motor cortex has been found to be aneffective area to place the sensing electrodes. This is because themotor cortex is further away from the surface of the brain, andtherefore signals received from the motor cortex contain fewer artifactsthan signals collected from other areas of the brain. To obtainadditional useful information relating to other conditions such asAlzheimer's and obesity, the sensing electrodes may also be located inother areas of the brain such as the prefrontal cortex. It is generallycontemplated that the sensing electrodes are implanted in the brain.Alternatively, the sensing electrodes may be placed upon the scalp ofthe user, for example an electrocorticography (ECoG) orelectroencephalography (EEG) electrode array.

The sensing electrodes can measure one or more different neural signals.These neural signals may include single unit action potentials,multi-unit activity, local field potential, signal power across at leastone frequency band, and coherence between signals. In additionalembodiments, the sensing electrodes or additional sensors also measurethe levels of one or more neurotransmitters. The levels of certainneurotransmitters may be particularly useful when treating certainillnesses. For example, monitoring dopamine levels is especially usefulin treating PD; and monitoring serotonin, dopamine, or norepinephrine isespecially useful in treating depression.

The measured neural signals can then be processed to obtain a “clean”signal. In this regard, for most purposes, it is desirable for eachelectrode to record the signal from a given neuron, rather than a set ofgiven neurons. The brain is very busy electrically, and the presence ofother neurons in the vicinity of these delicate and sensitive electrodescan create noise that obscures the desired signal. The signals actuallydetected by the electrode array are first amplified and then filtered toremove noise outside of a frequency band of interest (particularly inthe range of 0.3 Hz to 7.5 kHz). The signal may be processed in analogor digital form. Examples of useful analog filters include a 0.3 Hz 1storder high pass filter and a 7.5 kHz 3rd order low pass filter. Anexample of a digital filter is a digital 2.5 kHz 4th order Butterworthlow pass filter.

Part of the processing may involve artifact removal. Artifact removal isused to “clean up” the neural activity data and results in improvedsignals for later use. Artifacts in the data may have been caused by,for example, electrical stimulation of the brain. Identification of anartifact may be accomplished by, for example, by detecting peaks above athreshold crossing in the signal data. The threshold can be fixed ordynamically calculated, and for example can be modified based on factorsalready known, such as when electrical stimulation is delivered to thebrain. A set time window of data may then be removed around the detectedartifact. The data is then realigned (e.g. voltage-wise) and thenstitched back together (e.g. time-wise). Once such artifacts areremoved, the signal is of much smaller magnitude but contains usefulinformation.

Of course, when data is being measured on multiple channels (e.g. from a96-channel microelectrode array), the artifact should be removed on eachchannel. One common method of artifact removal is to determine theaverage from all or most of the channels, and then subtract the averagefrom each channel. Alternatively, the stimulation signal can be shapedin such a way that artifacts on certain frequencies may be prevented orreduced. In other embodiments, the artifacts can be planned somewhat.For example, the electrical stimulation delivered might have a knownshape, such as a square pulse. These will aid in identifying andremoving artifacts from the neural signals.

As previously mentioned, the measured neural signals are sent to theFeature Generator 120 and the Brain Network Observer 150. Referringstill to FIG. 1, the Brain Network Observer 150 is a separate computermodule that receives input from the sensing electrodes. The BrainNetwork Observer includes a Brain Network Model, which acts as asimulated model of the brain and its various neurons, including thosewhich are being measured directly using the sensing electrodes and thoseneurons which are not being measured directly using the sensingelectrodes. In particular, based on the actual neural signals that aremeasured, the Brain Network Model estimates the neural signals thatwould be present in neurons not directly measured. The estimated neuralsignals 113 are the same as those that are measured directly, as listedabove. This provides a more “global” view of the brain than would beavailable without the use of the BNM 150. This global view isadvantageous because many illnesses are caused by imbalances in a brainnetwork (e.g. pathways in the brain). These estimated neural signals 113are also sent to the Feature Generator 120.

Next, the Feature Generator (FG) 120 takes inputs from the measuredneural signals 112 and the estimated neural signals 113 and extractsfeatures from them. “Feature” is the term given to various pieces ofdata, either from each electrode individually or some/all electrodesconsidered as a group, that can contain useful information fordetermining the desired movement. Examples of such features include:signal amplitude, amplitude of the signal in a given frequency range,amplitude of the signal in a wavelet scale, or a firing rate. Here, afiring rate may refer to the number of action potentials per unit oftime for a single neuron. Other features may include single unit (SU)action potentials, multi-unit activity (MU), local field potential(LFP), signal power (e.g. across a frequency band or bands), andcoherence between signals. As used herein, SU relates toelectro-physiological responses of a single neuron; LFP relates to anelectrophysiological signal generated by the summed electric currentflowing from multiple nearby neurons within a small volume of nervoustissue; and MU relates to the activity of several nearby cells whenmeasured simultaneously.

In some applications, a Fast Fourier Transform (FFT) may be used toextract features. Advantageously, an FFT may be used for obtaining powerinformation. Additionally, a nonlinear or linear transform may be use tomap the features to N-dimensional space (e.g. by use of a radial basisfunction). This may be useful when a particular condition can manifestitself in the form of multiple different electrical signals from theelectrodes, so that the system can recognize any of those differentsignals.

The extracted features generated by the Feature Generator 120 are thensent as inputs to individual decoders 118. Each decoder has previouslybeen “trained” to associate certain features with a particular desiredfeedback. For example, a decoder may have been trained to calculate anindex measuring a particular condition, such as a condition severityindex. Other feedback index levels created by decoders can include animpulsive behavior index, a depressive state index, or an anxiety stateindex. The feedback index level calculated by a particular decoder canbe a binary output, or can have a range of outputs (e.g. a 1 to 5scale). It should be noted that a single decoder is also contemplated,although it is generally believed that multiple feedback index levelswill be calculated by the algorithm.

Next, the feedback index level(s) are sent to a Multiple Input MultipleOutput (MIMO) controller 122. The MIMO controller also receives thedesired index level 124 for each feedback index level received from thedecoders 118. For example, the decoder may calculate that the currentcondition severity index has a value of 5, and the desired index levelfor the condition severity index is a value of 3 or less. The MIMOcontroller generally calculates an error level between the feedbackindex level and the desired index level, and then determines anappropriate stimulation signal to the patient's brain that will minimizethe error level (i.e. ideally drive the error level to zero) over time.The MIMO controller can use adaptive or nonlinear algorithms as neededto provide robust control signals to the stimulating electrodes.

FIG. 2 illustrates the programming of the MIMO controller 122. One ormore feedback index levels 210 are received from decoders. Thesefeedback index levels 210 are compared with the desired index levels 124to generate an error level for each index, which is sent to adaptivecontroller 220. The adaptive controller 220 then generates thestimulation signals 114, which are sent to the electrodes and stimulatethe patient's brain. In some embodiments, the stimulation signals canalso be sent to the Brain Network Observer 150 as additional input forcalculation and consideration of the estimated neural signals.

The neural signals then measured by the sensing electrodes are sent tothe Brain Network Observer, resulting in closed loop feedback to controlsubsequent stimulation. The present disclosure permits high definitionstimulation. In high definition stimulation, the simulation pattern tothe stimulating electrodes is “continuously” updated. For example, thestimulation pattern to the electrodes is updated once every 0.1 seconds(e.g. 10 Hz). However, shorter and longer update times are alsocontemplated; in fact, speeds up to 50 Hz are contemplated. As discussedabove, the simulation pattern is provided based on the decoded indexlevels and is adjusted by the MIMO controller based on the informationdetermined by the Brain Network Observer.

As a result of the deep brain stimulation, desirably the neurologicaldisorder or its symptoms are minimized. The stimulation signal/patternsent to the electrodes can be changed continuously to obtain the desiredresult.

In FIG. 1, the Brain Network Observer 150 receives the measured neuralsignals 112 and outputs estimated neural signals 113. FIG. 3 illustratesadditional details of the operation of the Brain Network Observer 150module. Again, the Brain Network Model (BNM) 330 is a virtual model ofthe state of many neurons within the patient's brain, including thosethat are measured directly and those that are not measured directly.Based on the measured neural signals 112 for directly-measured neurons,the neural signals of non-measured neurons being modeled in the BNM areestimated. The estimation being carried by the BNM also includesestimates of the neural signals for the directly-measured neurons. Thoseestimated signals 310 can be directly compared to the measured neuralsignals for those particular neurons, and the difference between theestimated signals and the measured signals can be used to adjust theestimating algorithm 320 of the Brain Network Observer, so that theestimates become more accurate (i.e. the difference is reduced to zero).As the estimates become more accurate, the BNM and its generated outputwill become more accurate as well.

In order to successfully operate, the algorithms used in the variousmodules (decoders, Brain Network Observer) must be trained in how tointerpret the neural signals that are used as input. The multipledecoders 118 can be trained to output their feedback index level byusing data from accelerometers (e.g. for measuring the patient's bodytremors) or from a physician's assessment of the patient, imaginginformation, blood work, and other medical information. For example, ina treatment for depression, an fMRI (functional magnetic resonanceimaging) scan may provide an objective, numerical evaluation of thepatient's depression which is used to train the decoders. Decoders maybe trained by using any test that the physician runs. For example, thecondition severity index can be measured by having a physician observethe patient and assign a numerical score. The decoder thus receivesinformation that helps it to identify and assign a feedback level.Certain feedback signals may be related to a particular neurologicaldisorder. For example, a tremor severity/condition severity index may beuseful for treating PD; a depressive state index may be useful fortreating depression; an anxiety state index may be useful for treatinganxiety; and a pain index may be useful for treating chronic pain. Oncetrained, the decoders will no longer require manual intervention.

The Brain Network Model (BNM) is trained for each individual person, asdifferent people will of course have different neural signals foridentical conditions. The BNM itself can be validated using systemidentification and optimization techniques such as regression, geneticalgorithms, pattern search, Nelder-Mead etc. In regression techniques, amathematical relationship between dependent variables and independentvariables is created. For example, using a location of a neural signaland an amplitude of the neural signal, a mathematical relationship maybe created which is used to estimate an amplitude of another neuralsignal at another location. In genetic algorithms, rather than directlycreating a mathematical relationship, a population of candidatesolutions is “seeded” and then “evolved” through iterations towards anoptimum solution. In this regard, genetic algorithms mimic the processof natural selection. For example, a genetic algorithm may start with apopulation of ten randomly generated brain models. Then, during a firstiteration, the genetic algorithm may select the best fit brain modelfrom the population and randomly generate slight variations on thatbrain model to create a new population of ten brain models. This processmay continue to iterate until the selected brain model containscharacteristics that are within an acceptable range of desiredcharacteristics. Pattern search, on the other hand, is not an iterativemethod that converges to a solution. Pattern search refers to anumerical optimization method which has no requirement that any gradientof the problem be optimized. This allows pattern search methods to beused on functions that are not continuous or differentiable. In aNelder-Mead method, which is a numerical method, the minimum or maximummay be found of an objective function in a many-dimensional space. Thisrelies on the concept of a ‘simplex’ (a polytope of n +1 vertices in ndimensions). A segment on a line, a triangle on a plane, a tetrahedronin three-dimensional space are all examples of simplixes. TheNelder-Mead method operates by approximating a local optimum of aproblem with many variables when an objective function varies smoothlyand is unimodal.

The disclosed processing components (e.g. 116, 118, 120, 122 and 150 ofFIG. 1) are suitably embodied by an electronic data processing devicesuch as a computer or parallel computing system. Alternatively, they canbe embodied in hardware.

It will further be appreciated that the disclosed techniques may beembodied as a non-transitory storage medium storing instructionsreadable and executable by a computer, (microprocessor ormicrocontroller of an) embedded system, or various combinations thereof.The non-transitory storage medium may, for example, comprise a hard diskdrive, RAID or the like of a computer; an electronic, magnetic, optical,or other memory of an embedded system, or so forth.

The preferred embodiments have been illustrated and described.Obviously, modifications and alterations will occur to others uponreading and understanding the preceding detailed description. It isintended that the invention be construed as including all suchmodifications and alterations insofar as they come within the scope ofthe appended claims or the equivalents thereof.

1. A method for adjusting deep brain stimulation, comprising: (a)measuring neural signals in a target area of the brain of a user usingsensing electrodes; (b) providing the measured neural signals to aFeature Generator and to a Brain Network Model; (c) using the BrainNetwork Model to estimate neural signals not directly measurable by thesensing electrodes, and sending the estimated neural signals to theFeature Generator; (d) generating features from the measured neuralsignals and the estimated neural signals with the Feature Generator, andsending the features to at least one decoder; (e) calculating a feedbackindex level using output from the at least one decoder; (f) determiningan error level between the feedback index level and a desired indexlevel; (g) based on the error level, using a MIMO controller to send astimulation signal to stimulating electrodes in the target area of thebrain; and (h) repeating steps (a)-(g) until the error level between thefeedback index level and the desired index level is below a desiredvalue.
 2. The method of claim 1, wherein the measured neural signalsinclude single unit action potentials, multi-unit activity, local fieldpotential, signal power across at least one frequency band, andcoherence between signals.
 3. The method of claim 1, wherein the sensingelectrodes are implanted or are placed on the scalp of the user.
 4. Themethod of claim 1, wherein the generated features include signalamplitude, amplitude of the signal in a given frequency range, amplitudeof the signal in a wavelet scale, a firing rate, single unit actionpotentials, multi-unit activity, local field potential, signal poweracross at least one frequency band, and coherence between signals. 5.The method of claim 1, wherein the sensing electrodes are placed in thebasal ganglia, the motor cortex, the prefrontal cortex, the subthalamicnucleus, the external global pallidus, or the internal global pallidusof the user.
 6. The method of claim 1, further comprising measuring aneurotransmitter level, and sending the measured neurotransmitter levelto the at least one decoder.
 7. The method of claim 6, wherein theneurotransmitter is dopamine, serotonin, or norepinephrine.
 8. Themethod of claim 1, wherein the at least one decoder calculates acondition severity index, an impulsive behavior index, a depressivestate index, or an anxiety state index.
 9. The method of claim 1,wherein the stimulation signal is also sent to the Brain Network Model.10. The method of claim 1, wherein the Brain Network Model has beenvalidated using system identification and optimization techniques. 11.The method of claim 10, wherein the system identification andoptimization techniques are selected from the group consisting ofregression techniques, genetic algorithms, pattern searches, andNelder-Mead.
 12. The method of claim 1, wherein the at least one decoderhas been trained using a condition severity index, a depressive stateindex, or an anxiety state index determined by a physician.
 13. Themethod of claim 1, further comprising reducing a difference between themeasured neural signals and corresponding estimated neural signals byadjusting the Brain Network Model.
 14. A system for adjusting deep brainstimulation, comprising: sensing electrodes configured to measure neuralsignals in a target area of a brain of a patient; a Feature Generatorwhich receives the measured neural signals from the sensing electrodes;a Brain Network Model which receives the measured neural signals fromthe electrodes and estimates neural signals not directly measurable bythe sensing electrodes; multiple decoders which receive generatedfeatures from the Feature Generator and which calculate a feedback indexlevel; and a MIMO controller which determines an error between thefeedback index level and a desired index level and which send astimulation signal to stimulating electrodes based on the error; whereinthe MIMO controller is configured to continually determine a new errorbetween a new feedback index level and the desired index level and senda new stimulation signal to stimulating electrodes based on the newerror until the new error is below a desired value.